Qwen 2.5 VL 7B needs ~9.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
33K
Memory
9.5 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 98.0 tok/s | 1078 ms | 33K |
| Coding | A | Runs well | 98.0 tok/s | 1976 ms | 33K |
| Agentic Coding | A | Runs well | 98.0 tok/s | 2873 ms | 33K |
| Reasoning | A | Runs well | 98.0 tok/s | 2335 ms | 33K |
| RAG | A | Runs well | 98.0 tok/s | 3592 ms | 33K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen 2.5 VL 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 96.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 95.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 95.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 61.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 60.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.9 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 50.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 49.2 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen 2.5 VL 7B (7B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A73 |
Q3_K_S | 3 | 3.4 GB | Low | A73 |
NVFP4 | 4 | 3.9 GB | Medium | A73 |
Q4_K_M | 4 | 4.3 GB | Medium | A73 |
Q5_K_M | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | A74 |
Q8_0 | 8 | 7.5 GB | Very High | A74 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | A77 |
Copy-paste commands to run Qwen 2.5 VL 7B on your machine.
Run
lms load Qwen2.5-VL-7B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 91.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 39.5 tok/s | |
| Qwen 3.6 27B | 27B | S | 39.7 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 76.6 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 94.3 tok/s |
Yes, NVIDIA V100 32GB can run Qwen 2.5 VL 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.
Qwen 2.5 VL 7B (7B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 VL 7B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, Qwen 2.5 VL 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 VL 7B on NVIDIA V100 32GB receives a A grade with 98.0 tok/s and 33K context.
On NVIDIA V100 32GB, Qwen 2.5 VL 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-vl-7b-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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